ML_Toolbox_Machining

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ML_Toolbox_Machining

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# Machine Learning Toolbox for Machining
This toolbox includes the documentation for the Python codes used to diagnose chatter in machining applications. 
Wavelet Packet Transform (WPT), Ensemble Emprical Mode Decomposition (EEMD) and the Dynamic Time Warping (DTW) are the approaches included in this repository. Please see the references below for more details about the approaches.

The experimental data in both raw and processed format can be found in [Mendeley repository](https://data.mendeley.com/datasets/hvm4wh3jzx/1).

Sphinx documentation for this toolbox is available in this [link](http://firaskhasawneh.com/assets/repo_docs/ML_WPT_EEMD_doc/index.html).

**Note**: If you are using this toolbox, please cite these papers:

1. [Yesilli, Khasawneh, Otto, "On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition", 2020](https://www.sciencedirect.com/science/article/pii/S1755581719300690)
2. [Yesilli, Khasawneh, Otto, "Chatter detection in turning using machine learning and similarity measures of time series via dynamic time warping", 2022](https://www.sciencedirect.com/science/article/pii/S1526612522001682)

**Note**: Check build folder for the most up-to-date documentation. We will include the documentation in a website soon.

ML_Toolbox_Machining

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